Preference Mapping Techniques and segmentation of consumers

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Description

Performs preference mapping techniques based on multidimensional exploratory data analysis and segmentation of consumers.

Usage

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cartoconsumer(res, data.pref, nb.clust=0, seuil=0.8, consol=TRUE, ncp=5,
		 scale.conso=TRUE,graph.carto=TRUE,graph.hcpc=FALSE, graph.group=FALSE,
		col.min=7.5, col.max=0, contrast=0.2, level=0, asp=0,lwd=2)
				

Arguments

res

the result of a factor analysis

data.pref

a data frame in which each row represent a product and each column represent the hedonic scores of a given consumer for the products

nb.clust

an integer. If 0, the tree is cut at the level the user clicks on. If -1, the tree is automatically cut at the suggested level (see details). If a (positive) integer, the tree is cut with nb.cluters clusters

seuil

the size of the area kept for each group of consumers

consol

a boolean. If TRUE, a k-means consolidation is performed

ncp

number of dimensions kept in the results (by default 5)

scale.conso

scale data by consumer

graph.carto

if TRUE, the preference map is displayed. If FALSE, no graph is displayed

graph.hcpc

if TRUE, graphics of segmentation (trees and indivuals map) are displayed. If FALSE, no graph are displayed

graph.group

if TRUE, preference maps for each group are displayed. If FALSE, no map are displayed

col.min

define the color which match to the low levels of preference

col.max

define the color which match to the high levels of preference

contrast

define the color contrast between groups' areas and the rest of the map

level

the number of standard deviations used in the calculation of the preference response surface for all the consumers

asp

if 1 is assigned to that parameter, the graphic displays are output in an orthonormal coordinate system

lwd

control the line width for the outlines defining groups' areas

Details

The preference mapping methods are commonly used in the fields of market research and research and development to explore and understand the structure and tendencies of consumer preferences, to link consumer preference information to other data and to predict the behavior of consumers in terms of acceptance of a given product.
This function refers to the method introduced by M. Danzart. A segmentation of consumers is performed, and a preference map is displayed for each group of consumers. The original preference map is built, the areas of each group are underlined thanks to a contrast, and the number of consumers is shown.

Author(s)

Francois Husson husson@agrocampus-rennes.fr
Sophie Birot and Celia Pontet

References

Danzart M., Sieffermann J.M., Delarue J. (2004). New developments in preference mapping techniques: finding out a consumer optimal product, its sensory profile and the key sensory attributes. 7th Sensometrics Conference, July 27-30, 2004, Davis, CA.

See Also

MFA, GPA, carto

Examples

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## Not run: 
## Example 1: carto on the sensory descriptors
data(cocktail)
res.pca <- PCA(senso.cocktail)
results1 <- cartoconsumer(res.pca, hedo.cocktail)
results2 <- cartoconsumer(res.pca, hedo.cocktail,
      graph.hcpc=TRUE,graph.group=TRUE)

## End(Not run)

## Example 2
## Not run: 
data(cocktail)
res.mfa <- MFA(cbind.data.frame(senso.cocktail,compo.cocktail),
    group=c(ncol(senso.cocktail),ncol(compo.cocktail)),
    name.group=c("senso","compo"))
results3 <- cartoconsumer(res.mfa, hedo.cocktail)

## End(Not run)

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